One of the objectives of our work is to learn task-agnostic high-quality visual features from random internet image in the wild. To this end, we evaluate the quality of visual representations learned by our models during pretraining on a variety of datasets in computer vision [1]}. There are two widely used protocols for evaluating the visual features quality: linear-probe and full-finetuning. While it has been proven that fine-tuning exceeds the performance of linear classifiers [1]}, for our benchmarking, we choose linear-probe protocol. We make this choice because full-finetuning adapts visual features to each dataset and can compensate for and potentially mask the failures to learn general and robust representations during the pre-training. However, linear classifiers can highlight these failures which provides a better measure of the features quality.
